SQL Server implements three different physical operators to perform joins. In this session, we will examine how each of these operators works, including its advantages and challenges. Using real life examples, we will better understand the logic behind the optimizer's decisions on which operator to use for various joins. Finally, we will learn how to avoid some common join related pitfalls and how to get better performance from our queries.

Ami LevinAmi is a Microsoft SQL Server MVP, with over 20 years of experience in the IT industry. For the past 12 years, he has been consulting, teaching and speaking on SQL Server worldwide. He manages the Israeli SQL Server user group, leads the local support forum, and is a regular speaker at Microsoft conferences. Ami is the CTO and co-founder of DBSophic, a company that develops workload tuning solutions for SQL Server applications.

SQL Server 2005 introduced Dynamic Management Views (DMVs) that allow you to see exactly what is happening inside your SQL Server instances and databases with much more detail than ever before. SQL Server 2008 R2 adds even more capability in this area. You can discover your top wait types, most CPU intensive stored procedures, find missing indexes, and identify unused indexes, to name just a few examples. This session (which is applicable to both 2005, 2008 and 2008 R2), presents and explains over thirty DMV queries that you can quickly and easily use to detect and diagnose performance issues in your environment.

Glenn BerryGlenn works as a Database Architect at NewsGator Technologies in Denver, CO. He is a SQL Server MVP, and he has a whole collection of Microsoft certifications, including MCITP, MCDBA, MCSE, MCSD, MCAD, and MCTS, which proves that he likes to take tests. His expertise includes DMVs, high availability, full text search, and SQL Azure.

In complex Predictive Analytics (PA) scenarios where it is being applied to a complex system or the players involved are actually trying to undermine the predictions (ex: credit card fraud), the sophistication of the PA must be taken up a notch or two. In this Webcast I will describe techniques for building sophisticated PA systems on the Microsoft BI Stack by using OLAP to: - Analyze, validate, and optimize PA models. - Manage and Monitor the performance of the PA models in a Performance Management style. - Surface PA results to end users in a manner that allows them to work through the ambiguity that remains around predictions.

Eugene AsaharaEugene specializes in high-end Analysis Services implementation and performance tuning, predictive analytics, and overall BI architecture. He has thirteen years of experience on the Microsoft BI stack; including one year on the SSAS product team and seven years as a Lead DB/BI Architect at Microsoft Consulting Services. Aside from consulting on BI engagements by day Eugene develops bleeding-edge BI software by night. Many of his thoughts around BI and that bleeding-edge space can be found at www.softcodedlogic.com.